Sparsity‐based acoustic inversion in cross‐sectional multiscale optoacoustic imaging.

source: © 2015  Medical physics & American Association of Physicists in Medicine

With recent advancement in hardware of optoacoustic imaging systems, highly detailed cross‐sectional images may be acquired at a single laser shot, thus eliminating motion artifacts. Nonetheless, other sources of artifacts remain due to signal distortion or out‐of‐plane signals. The purpose of image reconstruction algorithms is to obtain the most accurate images from noisy, distorted projection data.

In this paper, the authors use the model‐based approach for acoustic inversion, combined with a sparsity‐based inversion procedure. Specifically, a cost function is used that includes the L1 norm of the image in sparse representation and a total variation (TV) term. The optimization problem is solved by a numerically efficient implementation of a nonlinear gradient descent algorithm. TV–L1 model‐based inversion is tested in the cross section geometry for numerically generated data as well as for in vivo experimental data from an adult mouse.

In all cases, model‐based TV–L1 inversion showed a better performance over the conventional Tikhonov regularization, TV inversion, and L1 inversion. In the numerical examples, the images reconstructed with TV–L1 inversion were quantitatively more similar to the originating images. In the experimental examples, TV–L1 inversion yielded sharper images and weaker streak artifact.

The results herein show that TV–L1 inversion is capable of improving the quality of highly detailed, multiscale optoacoustic images obtained in vivo using cross‐sectional imaging systems. As a result of its high fidelity, model‐based TV–L1 inversion may be considered as the new standard for image reconstruction in cross‐sectional imaging. [Read more……]

Experimental data reconstructions in almost-completed-view

Fig. Experimental data reconstructions in almost-completed-view with (a) Tik–Lap, (b) TV, (c) L1, and (d) TV–L1; (e)–(h) zoomed images in the dash line rectangle region of (a), (d), (c), and (d); (i) comparison of FWHM of the vessel along the dashed lines in (e)–(h).

Yiyong Han, Stratis Tzoumas, Antonio Nunes, Vasilis Ntziachristos, Amir Rosenthal.  Medical physics.